{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "3"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 1. Meet Professor William Sharpe\n",
    "<p>An investment may make sense if we expect it to return more money than it costs. But returns are only part of the story because they are risky - there may be a range of possible outcomes. How does one compare different investments that may deliver similar results on average, but exhibit different levels of risks?</p>\n",
    "<p><img style=\"float: left ; margin: 5px 20px 5px 1px;\" width=\"200\" src=\"https://assets.datacamp.com/production/project_66/img/sharpe.jpeg\"></p>\n",
    "<p>Enter William Sharpe. He introduced the <a href=\"https://web.stanford.edu/~wfsharpe/art/sr/sr.htm\"><em>reward-to-variability ratio</em></a> in 1966 that soon came to be called the Sharpe Ratio. It compares the expected returns for two investment opportunities and calculates the additional return per unit of risk an investor could obtain by choosing one over the other. In particular, it looks at the difference in returns for two investments and compares the average difference to the standard deviation (as a measure of risk) of this difference. A higher Sharpe ratio means that the reward will be higher for a given amount of risk. It is common to compare a specific opportunity against a benchmark that represents an entire category of investments.</p>\n",
    "<p>The Sharpe ratio has been one of the most popular risk/return measures in finance, not least because it's so simple to use. It also helped that Professor Sharpe won a Nobel Memorial Prize in Economics in 1990 for his work on the capital asset pricing model (CAPM).</p>\n",
    "<p>The Sharpe ratio is usually calculated for a portfolio and uses the risk-free interest rate as benchmark. We will simplify our example and use stocks instead of a portfolio. We will also use a stock index as benchmark rather than the risk-free interest rate because both are readily available at daily frequencies and we do not have to get into converting interest rates from annual to daily frequency. Just keep in mind that you would run the same calculation with portfolio returns and your risk-free rate of choice, e.g, the <a href=\"https://fred.stlouisfed.org/series/TB3MS\">3-month Treasury Bill Rate</a>. </p>\n",
    "<p>So let's learn about the Sharpe ratio by calculating it for the stocks of the two tech giants Facebook and Amazon. As benchmark we'll use the S&amp;P 500 that measures the performance of the 500 largest stocks in the US. When we use a stock index instead of the risk-free rate, the result is called the Information Ratio and is used to benchmark the return on active portfolio management because it tells you how much more return for a given unit of risk your portfolio manager earned relative to just putting your money into a low-cost index fund.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "dc": {
     "key": "3"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [],
   "source": [
    "# Importing required modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Settings to produce nice plots in a Jupyter notebook\n",
    "plt.style.use('fivethirtyeight')\n",
    "%matplotlib inline\n",
    "\n",
    "# Reading in the data\n",
    "stock_data = pd.read_csv(\"datasets/stock_data.csv\",parse_dates=['Date'],index_col=['Date']).dropna()\n",
    "benchmark_data = pd.read_csv(\"datasets/benchmark_data.csv\",parse_dates=['Date'],index_col=['Date']).dropna()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "11"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 2. A first glance at the data\n",
    "<p>Let's take a look the data to find out how many observations and variables we have at our disposal.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "dc": {
     "key": "11"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stocks\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 252 entries, 2016-01-04 to 2016-12-30\n",
      "Data columns (total 2 columns):\n",
      "Amazon      252 non-null float64\n",
      "Facebook    252 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 5.9 KB\n",
      "None\n",
      "                Amazon    Facebook\n",
      "Date                              \n",
      "2016-01-04  636.989990  102.220001\n",
      "2016-01-05  633.789978  102.730003\n",
      "2016-01-06  632.650024  102.970001\n",
      "2016-01-07  607.940002   97.919998\n",
      "2016-01-08  607.049988   97.330002\n",
      "\n",
      "Benchmarks\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 252 entries, 2016-01-04 to 2016-12-30\n",
      "Data columns (total 1 columns):\n",
      "S&P 500    252 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.9 KB\n",
      "None\n",
      "            S&P 500\n",
      "Date               \n",
      "2016-01-04  2012.66\n",
      "2016-01-05  2016.71\n",
      "2016-01-06  1990.26\n",
      "2016-01-07  1943.09\n",
      "2016-01-08  1922.03\n"
     ]
    }
   ],
   "source": [
    "# Display summary for stock_data\n",
    "print('Stocks\\n')\n",
    "# ... YOUR CODE FOR TASK 2 HERE ...\n",
    "print(stock_data.info())\n",
    "print(stock_data.head())\n",
    "# Display summary for benchmark_data\n",
    "print('\\nBenchmarks\\n')\n",
    "# ... YOUR CODE FOR TASK 2 HERE ...\n",
    "print(benchmark_data.info())\n",
    "print(benchmark_data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "18"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 3. Plot & summarize daily prices for Amazon and Facebook\n",
    "<p>Before we compare an investment in either Facebook or Amazon with the index of the 500 largest companies in the US, let's visualize the data, so we better understand what we're dealing with.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "dc": {
     "key": "18"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amazon</th>\n",
       "      <th>Facebook</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>699.523135</td>\n",
       "      <td>117.035873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>92.362312</td>\n",
       "      <td>8.899858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>482.070007</td>\n",
       "      <td>94.160004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>606.929993</td>\n",
       "      <td>112.202499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>727.875000</td>\n",
       "      <td>117.765000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>767.882492</td>\n",
       "      <td>123.902503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>844.359985</td>\n",
       "      <td>133.279999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Amazon    Facebook\n",
       "count  252.000000  252.000000\n",
       "mean   699.523135  117.035873\n",
       "std     92.362312    8.899858\n",
       "min    482.070007   94.160004\n",
       "25%    606.929993  112.202499\n",
       "50%    727.875000  117.765000\n",
       "75%    767.882492  123.902503\n",
       "max    844.359985  133.279999"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the stock_data\n",
    "# ... YOUR CODE FOR TASK 3 HERE ...\n",
    "stock_data.plot(title=\"Stock Data\",subplots=True)\n",
    "\n",
    "\n",
    "# summarize the stock_data\n",
    "# ... YOUR CODE FOR TASK 3 HERE ...\n",
    "stock_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "25"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 4. Visualize & summarize daily values for the S&P 500\n",
    "<p>Let's also take a closer look at the value of the S&amp;P 500, our benchmark.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "dc": {
     "key": "25"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S&amp;P 500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>252.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>2094.651310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>101.427615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1829.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2047.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>2104.105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>2169.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2271.720000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           S&P 500\n",
       "count   252.000000\n",
       "mean   2094.651310\n",
       "std     101.427615\n",
       "min    1829.080000\n",
       "25%    2047.060000\n",
       "50%    2104.105000\n",
       "75%    2169.075000\n",
       "max    2271.720000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the benchmark_data\n",
    "# ... YOUR CODE FOR TASK 4 HERE ...\n",
    "benchmark_data.plot(title=\"S&P 500\")\n",
    "\n",
    "# summarize the benchmark_data\n",
    "# ... YOUR CODE FOR TASK 4 HERE ...\n",
    "benchmark_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "32"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 5. The inputs for the Sharpe Ratio: Starting with Daily Stock Returns\n",
    "<p>The Sharpe Ratio uses the difference in returns between the two investment opportunities under consideration.</p>\n",
    "<p>However, our data show the historical value of each investment, not the return. To calculate the return, we need to calculate the percentage change in value from one day to the next. We'll also take a look at the summary statistics because these will become our inputs as we calculate the Sharpe Ratio. Can you already guess the result?</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "dc": {
     "key": "32"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amazon</th>\n",
       "      <th>Facebook</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.000818</td>\n",
       "      <td>0.000626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.018383</td>\n",
       "      <td>0.017840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-0.076100</td>\n",
       "      <td>-0.058105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-0.007211</td>\n",
       "      <td>-0.007220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.000879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.009224</td>\n",
       "      <td>0.008108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>0.095664</td>\n",
       "      <td>0.155214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Amazon    Facebook\n",
       "count  251.000000  251.000000\n",
       "mean     0.000818    0.000626\n",
       "std      0.018383    0.017840\n",
       "min     -0.076100   -0.058105\n",
       "25%     -0.007211   -0.007220\n",
       "50%      0.000857    0.000879\n",
       "75%      0.009224    0.008108\n",
       "max      0.095664    0.155214"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate daily stock_data returns\n",
    "stock_returns =stock_data.pct_change()\n",
    "\n",
    "# plot the daily returns\n",
    "# ... YOUR CODE FOR TASK 5 HERE ...\n",
    "stock_returns.plot()\n",
    "\n",
    "# summarize the daily returns\n",
    "# ... YOUR CODE FOR TASK 5 HERE ...\n",
    "stock_returns.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "39"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 6. Daily S&P 500 returns\n",
    "<p>For the S&amp;P 500, calculating daily returns works just the same way, we just need to make sure we select it as a <code>Series</code> using single brackets <code>[]</code> and not as a <code>DataFrame</code> to facilitate the calculations in the next step.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "dc": {
     "key": "39"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    251.000000\n",
       "mean       0.000458\n",
       "std        0.008205\n",
       "min       -0.035920\n",
       "25%       -0.002949\n",
       "50%        0.000205\n",
       "75%        0.004497\n",
       "max        0.024760\n",
       "Name: S&P 500, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate daily benchmark_data returns\n",
    "# ... YOUR CODE FOR TASK 6 HERE ...\n",
    "sp_returns = benchmark_data['S&P 500'].pct_change()\n",
    "\n",
    "# plot the daily returns\n",
    "# ... YOUR CODE FOR TASK 6 HERE ...\n",
    "sp_returns.plot()\n",
    "\n",
    "# summarize the daily returns\n",
    "# ... YOUR CODE FOR TASK 6 HERE ...\n",
    "sp_returns.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "46"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 7. Calculating Excess Returns for Amazon and Facebook vs. S&P 500\n",
    "<p>Next, we need to calculate the relative performance of stocks vs. the S&amp;P 500 benchmark. This is calculated as the difference in returns between <code>stock_returns</code> and <code>sp_returns</code> for each day.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "dc": {
     "key": "46"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amazon</th>\n",
       "      <th>Facebook</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.000168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.016126</td>\n",
       "      <td>0.015439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-0.100860</td>\n",
       "      <td>-0.051958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-0.006229</td>\n",
       "      <td>-0.005663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>-0.000454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.007351</td>\n",
       "      <td>0.005814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>0.100728</td>\n",
       "      <td>0.149686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Amazon    Facebook\n",
       "count  251.000000  251.000000\n",
       "mean     0.000360    0.000168\n",
       "std      0.016126    0.015439\n",
       "min     -0.100860   -0.051958\n",
       "25%     -0.006229   -0.005663\n",
       "50%      0.000698   -0.000454\n",
       "75%      0.007351    0.005814\n",
       "max      0.100728    0.149686"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the difference in daily returns\n",
    "excess_returns = stock_returns.sub(sp_returns,axis=0)\n",
    "\n",
    "# plot the excess_returns\n",
    "# ... YOUR CODE FOR TASK 7 HERE ...\n",
    "excess_returns.plot()\n",
    "\n",
    "# summarize the excess_returns\n",
    "# ... YOUR CODE FOR TASK 7 HERE ...\n",
    "excess_returns.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "53"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 8. The Sharpe Ratio, Step 1: The Average Difference in Daily Returns Stocks vs S&P 500\n",
    "<p>Now we can finally start computing the Sharpe Ratio. First we need to calculate the average of the <code>excess_returns</code>. This tells us how much more or less the investment yields per day compared to the benchmark.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "dc": {
     "key": "53"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2255e6f77c8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the mean of excess_returns \n",
    "# ... YOUR CODE FOR TASK 8 HERE ...\n",
    "avg_excess_return = excess_returns.mean()\n",
    "# plot avg_excess_returns\n",
    "# ... YOUR CODE FOR TASK 8 HERE ...\n",
    "avg_excess_return.plot.bar(title='Mean of the Return Difference')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "60"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 9. The Sharpe Ratio, Step 2: Standard Deviation of the Return Difference\n",
    "<p>It looks like there was quite a bit of a difference between average daily returns for Amazon and Facebook.</p>\n",
    "<p>Next, we calculate the standard deviation of the <code>excess_returns</code>. This shows us the amount of risk an investment in the stocks implies as compared to an investment in the S&amp;P 500.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "dc": {
     "key": "60"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the standard deviations\n",
    "sd_excess_return = excess_returns.std()\n",
    "\n",
    "# plot the standard deviations\n",
    "# ... YOUR CODE FOR TASK 9 HERE ...\n",
    "sd_excess_return.plot.bar(title='Standard Deviation of the Return Difference');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "67"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 10. Putting it all together\n",
    "<p>Now we just need to compute the ratio of <code>avg_excess_returns</code> and <code>sd_excess_returns</code>. The result is now finally the <em>Sharpe ratio</em> and indicates how much more (or less) return the investment opportunity under consideration yields per unit of risk.</p>\n",
    "<p>The Sharpe Ratio is often <em>annualized</em> by multiplying it by the square root of the number of periods. We have used daily data as input, so we'll use the square root of the number of trading days (5 days, 52 weeks, minus a few holidays): √252</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "dc": {
     "key": "67"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the daily sharpe ratio\n",
    "daily_sharpe_ratio =daily_sharpe_ratio = avg_excess_return.div(sd_excess_return)\n",
    "\n",
    "# annualize the sharpe ratio\n",
    "annual_factor =np.sqrt(252)\n",
    "annual_sharpe_ratio = daily_sharpe_ratio.mul(annual_factor)\n",
    "\n",
    "# plot the annualized sharpe ratio\n",
    "# ... YOUR CODE FOR TASK 10 HERE ...\n",
    "annual_sharpe_ratio.plot.bar(title='Annualized Sharpe Ratio: Stocks vs S&P 500');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "74"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 11. Conclusion\n",
    "<p>Given the two Sharpe ratios, which investment should we go for? In 2016, Amazon had a Sharpe ratio twice as high as Facebook. This means that an investment in Amazon returned twice as much compared to the S&amp;P 500 for each unit of risk an investor would have assumed. In other words, in risk-adjusted terms, the investment in Amazon would have been more attractive.</p>\n",
    "<p>This difference was mostly driven by differences in return rather than risk between Amazon and Facebook. The risk of choosing Amazon over FB (as measured by the standard deviation) was only slightly higher so that the higher Sharpe ratio for Amazon ends up higher mainly due to the higher average daily returns for Amazon. </p>\n",
    "<p>When faced with investment alternatives that offer both different returns and risks, the Sharpe Ratio helps to make a decision by adjusting the returns by the differences in risk and allows an investor to compare investment opportunities on equal terms, that is, on an 'apples-to-apples' basis.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "dc": {
     "key": "74"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [],
   "source": [
    "# Uncomment your choice.\n",
    "buy_amazon = True\n",
    "# buy_facebook = True"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
